Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses' role in population health management

The use of individual and multilevel data in the development of a risk prediction model to predict patients' likelihood of completing colorectal cancer screening

Promotion of colorectal cancer (CRC) screening can be expensive and unnecessary for many patients. The use of predictive analytics promises to help health systems target the right services to the right patients at the right time while improving population health. Multilevel data at the interpersonal, organizational, community, and policy levels, is rarely considered in clinical decision making but may be used to improve CRC screening risk prediction. We compared the effectiveness of a CRC screening risk prediction model that uses multilevel data with a more conventional model that uses only individual patient data. We used a retrospective cohort to ascertain the one-year occurrence of CRC screening. The cohort was determined from a Health Maintenance Organization, in Oregon. Eligible patients were 50-75 years old, health plan members for at least one year before their birthday in 2018 and were due for screening. We created a risk model using logistic regression first with data available in the electronic health record (EHR), and then added multilevel data. In a cohort of 59,249 patients, 36.1% completed CRC screening. The individual level model included 14 demographic, clinical and encounter based characteristics, had a bootstrap-corrected C-statistic of 0.722 and sufficient calibration. The multilevel model added 9 variables from clinical setting and community characteristics, and the bootstrap-corrected C-statistic remained the same with continued sufficient calibration. The predictive power of the CRC screening model did not improve after adding multilevel data. Our findings suggest that multilevel data added no improvement to the prediction of the likelihood of CRC screening.

Comparing and validating medication complexity from insurance claims against electronic health records

BACKGROUND: Patient effort to comply with complex medication instructions is known to be related to nonadherence and subsequent medical complications or health care costs. A widely used Medication Regimen Complexity Index (MRCI) has been used with electronic health records (EHRs) to identify patients who could benefit from pharmacist intervention. A similar claims-derived measure may be better suited for clinical decision support, since claims offer a more complete view of patient care and health utilization. OBJECTIVE: To define and validate a novel insurance claims-based medication complexity score (MCS) patterned after the widely used MRCI, derived from EHRs. METHODS: Insurance claims and EHR data were provided by HealthPartners (N = 54,988) (Bloomington, Minnesota) and The Johns Hopkins Health System (N = 28,589) (Baltimore, Maryland) for years 2013 and 2017, respectively. Yearly measures of medication complexity were developed for each patient and evaluated with one another using rank correlation within different clinical subgroupings. Indicators for the presence of individually complex prescriptions were also developed and assessed using exact agreement. Complexity measures were then correlated with select covariates to further validate the concordance between MCS and MRCI with respect to clinical metrics. These included demographic, comorbidity, and health care utilization markers. Prescribed medications in each system's EHR were coded using the previously validated MRCI weighting rules. Insurance claims for retail pharmacy medications were coded using our novel MCS, which closely followed MRCI scoring rules. RESULTS: EHR-based MRCI and claims-based MCS were significantly correlated with one another for most clinical subgroupings. Likewise, both measures were correlated with several covariates, including count of active medications and chronic conditions. The MCS was, in most cases, more associated with key health covariates than was MRCI, although both were consistently significant. We found that the highest correlation between MCS and MRCI is obtained with patients who have similar counts of pharmacy records between EHRs and claims (HealthPartners: P = 0.796; Johns Hopkins Health System: P = 0.779). CONCLUSIONS: The findings suggest good correspondence between MCS and MRCI and that claims data represent a useful resource for assessing medication complexity. Claims data also have major practical advantages, such as interoperability across health care systems, although they lack the detailed clinical context of EHRs. DISCLOSURES: The Johns Hopkins University holds the copyright to the Adjusted Clinical Groups (ACG) system and receives royalties from the global distribution of the ACG system. This revenue supports a portion of the authors' salary. No additional or external funding supported this work. The authors have no conflict of interest to disclose.

Hospital Readmission Outcomes by Frailty Risk in Adults in Behavioral Health Acute Care

The purpose of the current retrospective study was to determine whether frailty is predictive of 30-day readmission in adults aged ≥50 years who were admitted with a psychiatric diagnosis to a behavioral health hospital from 2013 to 2017. A total of 1,063 patients were included. A 26-item frailty risk score (FRS-26-ICD) was constructed from electronic health record (EHR) data. There were 114 readmissions. Cox regression modeling for demographic characteristics, emergent admission, comorbidity, and FRS-26-ICD determined prediction of time to readmission was modest (incremental area under the receiver operating characteristic curve = 0.671). The FRS-26-ICD was a significant predictor of readmission alone and in models with demographics and emergent admission; however, only the Elixhauser Comorbidity Index was significantly related to hazard of readmission adjusting for other factors (adjusted hazard ratio = 1.26, 95% confidence interval [1.17, 1.37]; p < 0.001), whereas FRS-26-ICD became non-significant. Frailty is a relevant syndrome in behavioral health that should be further studied in risk prediction and incorporated into care planning to prevent hospital readmissions. [Journal of Psychosocial Nursing and Mental Health Services, xx(x), xx-xx.].

Using Health Plan Authorization Data to Predict Hospitalizations and Readmissions in a Combined Commercial and Medicaid Population

Health plans develop predictive models to predict key clinical events (eg, admissions, readmissions, emergency department visits). The authors developed predictive models of admissions and readmissions for a quality improvement organization with many large government and private health plan clients. Its membership and authorization data were used to develop models predicting 2019 inpatient stays, and 2019 readmissions following 2019 admissions, based on patients' age and sex, diagnoses identified and procedures requested in 2018 authorizations, and 2018 admission authorizations. In addition to testing multivariate models, risk scores were calculated for admission and readmission for all patients in the model. The admissions model (C = 0.8491) is much more accurate than the readmissions model (C = 0.6237). Measures of risk score central tendency and skewness indicate that the vast majority of members had little risk of hospitalization in 2019; the mean (standard deviation) was 0.042 (0.074), and the median was 0.018. These risk scores can be used to identify members at risk of admission and to support proactive risk management (eg, design of health management programs). Different risk thresholds can be used to identify different subsets of members for follow-up, depending on overall strategy and available resources. This model development project was novel in employing authorization data rather than utilization data. Advantages of authorization data are their timeliness, and the fact that they are sometimes the only data available, but disadvantages of authorization data are that authorized services are not always actually performed, and diagnoses are often "rule out" rather than final diagnoses.

Comparison of a Frailty Risk Score and Comorbidity Indices for Hospital Readmission Using Electronic Health Record Data

The purpose of the current study was to investigate the predictive properties of five definitions of a frailty risk score (FRS) and three comorbidity indices using data from electronic health records (EHRs) of hospitalized adults aged ≥50 years for 3-day, 7-day, and 30-day readmission, and to identify an optimal model for a FRS and comorbidity combination. Retrospective analysis of the EHR dataset was performed, and multivariable logistic regression and area under the curve (AUC) were used to examine readmission for frailty and comorbidity. The sample (N = 55,778) was mostly female (53%), non-Hispanic White (73%), married (53%), and on Medicare (55%). Mean FRSs ranged from 1.3 (SD = 1.5) to 4.3 (SD = 2.1). FRS and comorbidity were independently associated with readmission. Predictive accuracy for FRS and comorbidity combinations ranged from AUC of 0.75 to 0.77 (30-day readmission) to 0.84 to 0.85 (3-day readmission). FRS and comorbidity combinations performed similarly well, whereas comorbidity was always independently associated with readmission. FRS measures were more associated with 30-day readmission than 7-day and 3-day readmission. [Research in Gerontological Nursing, 14(2), 91-103.].

A Performance Management System in Healthcare for All Seasons?

Health systems face challenges which are inherent to care demand and supply evolution (i.e., demographic change, new technologies) or are the results of unexpected occurrence originating outside the health system, such as economic shocks or epidemic outbreaks. Both challenges often require a paradigm shift in governance and organization, financing and resource allocation, accountability frameworks, as well as public health system responses. Based on key reviews and seminal papers of performance management, public health, sustainability and resilience, the article presents three emerging challenges for performance management systems in healthcare: i) the inclusion of the population approach; ii) the measurement and consideration of the multi-facets concepts of value; iii) the importance of resilience and sustainability. Performance management systems need to evolve to cope with this changing scenario. The article sheds light on uncovered areas by performance management, and it proposes a research agenda for scholars of both performance management and health service research.